On Phase Information for Deep Neural Networks to Solve Full-Wave Nonlinear Inverse Scattering Problems
Xiao‐Min Pan, Bo-Yue Song, Di Wu, Guohua Wei, Xin‐Qing Sheng
Abstract
The phase information's role in deep neural networks (DNNs) to solve the electromagnetic inverse scattering problems is investigated. The feedforward neutral network model with complex-valued (CV) data stream and the corresponding CV backpropagation training algorithm are proposed to realize CV convolutional neural networks. Numerical examples are carried out to demonstrate the phase information's role in DNNs in terms of generalization capability as well as the convergence speed in the training stage.
Topics & Concepts
BackpropagationComputer scienceConvergence (economics)Artificial neural networkFeedforward neural networkConvolutional neural networkFeed forwardInverse scattering problemInverse problemGeneralizationNonlinear systemInversePhase (matter)AlgorithmScatteringDeep learningArtificial intelligenceMathematicsPhysicsMathematical analysisOpticsEngineeringControl engineeringEconomicsGeometryEconomic growthQuantum mechanicsMicrowave Imaging and Scattering AnalysisNumerical methods in inverse problemsGeophysical Methods and Applications